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Meeting 2022 TMS Annual Meeting & Exhibition
Symposium REWAS 2022: Automation and Digitalization for Advanced Manufacturing
Presentation Title Factors to Consider when Designing Aluminium Alloys for Increased Scrap Usage
Author(s) Luca Montanelli
On-Site Speaker (Planned) Luca Montanelli
Abstract Scope For a significant shift in alloy design to happen, the aluminium alloy industry needs to explore a broader range of compositional and processing dimensions. The proposed project will investigate unexplored regions of the compositional space to guide the design of new alloys, especially where opportunities are present to improve recyclability. To achieve this, a blending model will be optimised over compositional space to inform alloy compositions that enable higher quantities of scrap use. Blending models inform on the scrap usage of a candidate alloy when it is set in a predetermined market landscape. Due to their computational cost, machine learning optimisation methods such as Bayesian optimisation will be employed to focus the design process. The optimisation will be subject to constraints that are based on compositions, phase combinations, and relevant properties to ensure that the alloys not only maximise amount of scrap use but also meet technical requirements.
Proceedings Inclusion? Planned:
Keywords Recycling and Secondary Recovery, Other, Other

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

AI/Data Mining in Materials Manufacturing
Audio Signal Processing for Quantitative Moulding Material Regeneration
Computational Methodology to Simulate Pyrometallurgical Processes in a Secondary Lead Furnace
Determining the Bubble Dynamics of a Top Submerged Lance Smelter
Development of Virtual Die Casting Simulator for Workforce Development
Digitalization for Advanced Manufacturing through Simulation, Visualization and Machine Learning
Digitalizing the Circular Economy (CE): From Reactor Simulation to System Models of the CE
Evolution of Process Models to Digital Twins
Factors to Consider when Designing Aluminium Alloys for Increased Scrap Usage
NOW ON-DEMAND ONLY - An Automated Recycling Process of End-of-life Lithium-ion Batteries Enhanced by Online Sensing and Machine Learning Techniques
Refractory Lifetime Prediction in Industrial Processes with Artificial Intelligence
Steel Production Efficiency Improvements by Digitalization

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